Update generate.py

generate.py

@@ -8,41 +8,84 @@ from transformers.generation.utils import (
 
 logger = logging.get_logger(__name__)
 
-@torch.no_grad()
-def custom_generate(model, input_ids, attention_mask, max_new_tokens, streamer, **kwargs):
-    finished_generating = torch.zeros(len(input_ids), dtype=torch.bool, device=input_ids.device)
-    for cur_token_idx in range(max_new_tokens):
-        # Sample the next token
-        new_ids = model(
-            input_ids[~finished_generating],
-            attention_mask=attention_mask[~finished_generating]
-        )['logits']
-        # Mask out the start and end thought tokens so we don't accidentally sample them
-        new_ids[:, :, model.tokenizer.vocab_size:] = -float("inf")
-        for list_idx, answer_idx in enumerate((~finished_generating).nonzero(as_tuple=True)[0]):
-            # Find the index of the last token that is not padding
-            base_answer_ids = input_ids[answer_idx]
-            new_answer_ids = new_ids[list_idx]
-            last_token_idx = (base_answer_ids != model.tokenizer.pad_token_id).nonzero(as_tuple=True)[0].max()
-            new_ids_sampled = torch.multinomial(
-                torch.nn.functional.softmax(new_answer_ids[last_token_idx] / kwargs.get("temperature", 1.0), dim=-1), 1)
-            # Assign the new id to the last token
-            if last_token_idx + 1 >= len(base_answer_ids):
-                # Add padding everywhere
-                new_padding = torch.full((len(input_ids), 1), model.tokenizer.pad_token_id, dtype=torch.long,
-                                        device=input_ids.device)
-                input_ids = torch.cat([input_ids, new_padding], dim=-1)
-                attention_mask = torch.cat([attention_mask, torch.zeros_like(new_padding)], dim=-1)
-            attention_mask[answer_idx, last_token_idx + 1] = 1
-            input_ids[answer_idx, last_token_idx + 1] = new_ids_sampled
-            if new_ids_sampled == model.tokenizer.eos_token_id or new_ids_sampled == model.tokenizer.bos_token_id or new_ids_sampled == model.tokenizer.pad_token_id:
-                finished_generating[answer_idx] = 1
-            # Check if the end token is generated
-            if new_ids_sampled == model.tokenizer.convert_tokens_to_ids("<|/assistant|>"):
-                finished_generating[answer_idx] = 1
-        if finished_generating.all():
-            break
-        streamer.put(new_ids_sampled)
+def custom_generate(
+    self,
+    input_ids,
+    attention_mask=None,
+    max_length=None,
+    min_length=None,
+    do_sample=None,
+    early_stopping=None,
+    num_beams=None,
+    temperature=1.0,
+    top_k=None,
+    top_p=None,
+    repetition_penalty=None,
+    bad_words_ids=None,
+    bos_token_id=None,
+    pad_token_id=None,
+    eos_token_id=None,
+    length_penalty=None,
+    no_repeat_ngram_size=None,
+    num_return_sequences=None,
+    decoder_start_token_id=None,
+    use_cache=None,
+    num_beam_groups=None,
+    diversity_penalty=None,
+    prefix_allowed_tokens_fn=None,
+    output_attentions=None,
+    output_hidden_states=None,
+    output_scores=None,
+    return_dict_in_generate=None,
+    forced_bos_token_id=None,
+    forced_eos_token_id=None,
+    remove_invalid_values=None,
+    synced_gpus=None,
+    **kwargs,
+):
+    with torch.no_grad():
+        finished_generating = torch.zeros(len(input_ids), dtype=torch.bool, device=input_ids.device)
+
+        while not finished_generating.all() and input_ids.shape[1] < max_length:
+            # Sample the next token
+            new_ids = self(
+                input_ids[~finished_generating],
+                attention_mask=attention_mask[~finished_generating] if attention_mask is not None else None,
+                **kwargs
+            )['logits']
+
+            # Mask out the start and end thought tokens so we don't accidentally sample them
+            new_ids[:, :, self.tokenizer.vocab_size:] = -float("inf")
+
+            for list_idx, answer_idx in enumerate((~finished_generating).nonzero(as_tuple=True)[0]):
+                # Find the index of the last token that is not padding
+                base_answer_ids = input_ids[answer_idx]
+                new_answer_ids = new_ids[list_idx]
+                last_token_idx = (base_answer_ids != self.tokenizer.pad_token_id).nonzero(as_tuple=True)[0].max()
+
+                new_ids_sampled = torch.multinomial(
+                    torch.nn.functional.softmax(new_answer_ids[last_token_idx] / temperature, dim=-1), 1)
+
+                # Assign the new id to the last token
+                if last_token_idx + 1 >= len(base_answer_ids):
+                    # Add padding everywhere
+                    new_padding = torch.full((len(input_ids), 1), self.tokenizer.pad_token_id, dtype=torch.long,
+                                            device=input_ids.device)
+                    input_ids = torch.cat([input_ids, new_padding], dim=-1)
+                    if attention_mask is not None:
+                        attention_mask = torch.cat([attention_mask, torch.zeros_like(new_padding)], dim=-1)
+
+                if attention_mask is not None:
+                    attention_mask[answer_idx, last_token_idx + 1] = 1
+                input_ids[answer_idx, last_token_idx + 1] = new_ids_sampled
+
+                if new_ids_sampled == self.tokenizer.eos_token_id or new_ids_sampled == self.tokenizer.bos_token_id or new_ids_sampled == self.tokenizer.pad_token_id:
+                    finished_generating[answer_idx] = 1
+
+                # Check if the end token is generated
+                if new_ids_sampled == self.tokenizer.convert_tokens_to_ids("<|/assistant|>"):
+                    finished_generating[answer_idx] = 1
+
     return input_ids, attention_mask
 
 def generate(
@@ -78,57 +121,40 @@ def generate(
     forced_eos_token_id=None,
     remove_invalid_values=None,
     synced_gpus=None,
-    n_ahead=4,
-    n_ahead_talk=4,
-    merged_talk_heads=True,
-    merged_lm_and_talk_heads=False,
-    merged_lm_and_think_heads=True,
-    use_concat_talk_head=True,
-    use_shallow_think=True,
-    use_shallow_talk=False,
-    use_complex_think_head=False,
-    use_complex_talk_head=True,
-    use_weighted_talk_head=True,
-    trust_remote_code=True,
-    torch_dtype=torch.bfloat16,
     **model_kwargs,
 ):
-    # Set model attributes
-    self.max_thoughts = n_ahead + n_ahead_talk + 1
-    self.merged_talk_heads = merged_talk_heads
-    self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
-    self.merged_lm_and_think_heads = merged_lm_and_think_heads
-    self.use_concat_talk_head = use_concat_talk_head
-    self.use_shallow_think = use_shallow_think
-    self.use_shallow_talk = use_shallow_talk
-    self.use_complex_think_head = use_complex_think_head
-    self.use_complex_talk_head = use_complex_talk_head
-    self.use_weighted_talk_head = use_weighted_talk_head
-
-    # Set model properties
-    self.use_end_thought_token = True
-    self.use_start_thought_token = True
-    self.wandb_enabled = True
-    self.n_ahead = n_ahead
-    self.n_passes = 1
-    self.eval_mode = True
-    self.first_run = False
-    self.kill_after = 100
-    self.rm_initialized = True
-    self.original_mode = False
-
-    # Initialize a TextStreamer for streaming the generated text
-    streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-    # Generate using the custom generate function
-    input_ids, attention_mask = custom_generate(
+    return custom_generate(
         self,
-        input_ids,
-        attention_mask,
-        max_length,
-        streamer,
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        max_length=max_length,
+        min_length=min_length,
+        do_sample=do_sample,
+        early_stopping=early_stopping,
+        num_beams=num_beams,
         temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        bad_words_ids=bad_words_ids,
+        bos_token_id=bos_token_id,
+        pad_token_id=pad_token_id,
+        eos_token_id=eos_token_id,
+        length_penalty=length_penalty,
+        no_repeat_ngram_size=no_repeat_ngram_size,
+        num_return_sequences=num_return_sequences,
+        decoder_start_token_id=decoder_start_token_id,
+        use_cache=use_cache,
+        num_beam_groups=num_beam_groups,
+        diversity_penalty=diversity_penalty,
+        prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        output_scores=output_scores,
+        return_dict_in_generate=return_dict_in_generate,
+        forced_bos_token_id=forced_bos_token_id,
+        forced_eos_token_id=forced_eos_token_id,
+        remove_invalid_values=remove_invalid_values,
+        synced_gpus=synced_gpus,
         **model_kwargs,
-    )
-
-    return input_ids, attention_mask
+    )